Recombinant Desulfovibrio vulgaris Probable rRNA maturation factor (DVU_1883)

What is the Recombinant Desulfovibrio vulgaris Probable rRNA maturation factor (DVU_1883)?

Recombinant Desulfovibrio vulgaris Probable rRNA maturation factor (DVU_1883) is a full-length protein (162 amino acids) that functions in RNA processing and maturation. The protein is derived from Desulfovibrio vulgaris (strain Hildenborough / ATCC 29579 / DSM 644 / NCIMB 8303) and is available as a recombinant product expressed in either baculovirus or mammalian cell systems. Its UniProt accession number is Q72AV7, and it is also known by the target name ybeY.

What are the expression systems available for DVU_1883 production?

Based on the available data, DVU_1883 is produced in two primary expression systems:

Both expression systems yield the full-length protein (1-162 amino acids) with a purity greater than 85% as determined by SDS-PAGE. The choice between these systems may depend on specific experimental requirements, such as post-translational modifications or folding considerations.

What are the optimal storage conditions for DVU_1883?

The stability and shelf life of DVU_1883 depend on several factors including the formulation state, buffer components, storage temperature, and the inherent stability of the protein. For optimal preservation:

• Lyophilized form maintains stability for approximately 12 months when stored at -20°C to -80°C

• Liquid formulations remain stable for approximately 6 months at -20°C to -80°C

• Working aliquots can be stored at 4°C for up to one week

• Repeated freeze-thaw cycles should be avoided to maintain protein integrity

How should DVU_1883 be reconstituted for experimental use?

For proper reconstitution of the protein:

• Briefly centrifuge the vial prior to opening to bring contents to the bottom

• Reconstitute the protein in deionized sterile water to achieve a concentration of 0.1-1.0 mg/mL

• Add glycerol to a final concentration of 5-50% (50% is recommended as default)

• Aliquot the reconstituted protein to minimize freeze-thaw cycles

• Store aliquots at -20°C or -80°C for long-term stability

How should I design experiments to study the functional properties of DVU_1883?

When designing experiments to investigate the functional properties of DVU_1883, consider the following methodological approach:

• Define clear research questions and hypotheses: Formulate specific, testable hypotheses about the protein's role in rRNA maturation or related processes

• Identify appropriate variables:

  • Independent variables: protein concentration, reaction conditions, substrate variations

  • Dependent variables: rRNA processing efficiency, binding affinity, enzymatic activity

  • Control for extraneous variables such as buffer composition and experimental temperature

• Treatment design: Systematically manipulate independent variables to observe their effects on outcomes. For example:

  • Concentration gradient studies (0.1 μg/mL, 1.0 μg/mL, 10 μg/mL)

  • Temperature variation experiments (4°C, 25°C, 37°C)

  • pH response profiles (pH 6.0, 7.0, 8.0)

  • Substrate specificity assays with various RNA targets

• Randomization: Incorporate randomization in experimental design to minimize bias and ensure statistical validity of results

What control experiments should be included when working with DVU_1883?

Robust experimental design requires appropriate controls to validate findings and eliminate alternative explanations:

• Negative controls:

  • Buffer-only reactions (no protein)

  • Heat-inactivated protein preparations

  • Non-related proteins of similar size/structure

• Positive controls:

  • Well-characterized rRNA maturation factors

  • Previously validated substrates

  • Known interaction partners

• System controls:

  • Expression system background controls (baculovirus vs. mammalian)

  • Tag-only controls if tagged versions are used

  • Time-course stability controls

How can I study the structural-functional relationship of DVU_1883?

To investigate the structure-function relationships of DVU_1883, consider this methodological framework:

• Structural analysis approaches:

  • X-ray crystallography to determine three-dimensional structure

  • NMR spectroscopy for solution-state dynamics

  • Cryo-EM for complex formation visualization

  • In silico modeling based on homologous proteins

• Functional mapping strategies:

  • Site-directed mutagenesis of conserved residues

  • Domain deletion or swapping experiments

  • Chemical modification of specific amino acids

  • Cross-linking studies with potential binding partners

• Correlation methodology:

  • Analyze how structural modifications affect functional outcomes

  • Compare wild-type and mutant proteins using activity assays

  • Develop structure-activity relationship models based on experimental data

What approaches can be used to investigate DVU_1883 interactions with rRNA substrates?

To characterize DVU_1883 interactions with rRNA substrates:

• Binding assays:

  • Electrophoretic mobility shift assays (EMSA)

  • Surface plasmon resonance (SPR)

  • Fluorescence anisotropy measurements

  • Isothermal titration calorimetry (ITC)

• Functional interaction studies:

  • RNA processing assays with defined substrates

  • Competition experiments with related factors

  • Co-immunoprecipitation of protein-RNA complexes

  • In vivo complementation studies

• Data analysis and presentation:

  • Determine binding constants and kinetic parameters

  • Present binding curves showing concentration-dependent effects

  • Analyze specificity using multiple substrate variants

  • Compare data from multiple techniques for validation

How should data from DVU_1883 functional assays be analyzed and presented?

For rigorous analysis and clear presentation of DVU_1883 functional data:

• Statistical approach:

  • Apply appropriate statistical tests (t-tests, ANOVA) based on experimental design

  • Calculate confidence intervals and p-values for significant findings

  • Perform regression analysis for dose-dependent effects

  • Consider replicate variability and power analysis for sample size determination

• Data presentation best practices:

  • Present general findings first, followed by specific details

  • Use tables for precise numerical values and comparisons

  • Employ graphs for showing trends and relationships

  • Ensure all figures and tables are self-explanatory with clear titles and labels

Example table format for presenting activity data:

Note: Values represent mean ± standard deviation from three independent experiments.

How can I resolve contradictory data when studying DVU_1883 function?

When encountering contradictory results in DVU_1883 research:

• Systematic troubleshooting approach:

  • Verify protein quality and activity through multiple independent preparations

  • Examine differences in experimental conditions between contradictory datasets

  • Consider expression system differences (baculovirus vs. mammalian) as potential factors

  • Evaluate reagent quality and experimental timing

• Reconciliation strategies:

  • Design decisive experiments specifically targeting the contradictions

  • Employ multiple complementary techniques to address the same question

  • Consider biological relevance of observed differences (statistical vs. biological significance)

  • Consult with collaborators or external experts for independent validation

• Transparent reporting:

  • Document and report all contradictory findings

  • Discuss possible explanations for discrepancies

  • Propose reconciliation models that account for apparent contradictions

  • Outline future experiments that could resolve remaining questions

How does DVU_1883 compare to rRNA maturation factors in other organisms?

A comparative analysis of DVU_1883 with other rRNA maturation factors provides evolutionary context:

• Sequence comparison methodology:

  • Perform multiple sequence alignments with homologs from diverse species

  • Identify conserved domains and critical residues

  • Calculate sequence identity and similarity percentages

  • Construct phylogenetic trees to visualize evolutionary relationships

• Functional conservation analysis:

  • Compare substrate specificity across homologs

  • Evaluate complementation capacity in heterologous systems

  • Analyze structural conservation using homology modeling

  • Identify species-specific adaptations in sequence and function

• Evolutionary interpretation:

  • Discuss the implications of conservation patterns for protein function

  • Relate sequence divergence to ecological or metabolic adaptations

  • Propose evolutionary models for the development of rRNA maturation mechanisms

  • Identify potential horizontal gene transfer events if present in the evolutionary history

What are the methodological considerations for comparing baculovirus-expressed versus mammalian-expressed DVU_1883?

When comparing DVU_1883 produced in different expression systems:

• System-specific considerations:

  • Baculovirus expression may yield higher quantities but different post-translational modifications

  • Mammalian expression may provide more native-like modifications but at lower yields

  • Careful characterization of each preparation is essential before comparative studies

• Analytical comparison approach:

  • Perform side-by-side activity assays under identical conditions

  • Analyze structural integrity through circular dichroism or limited proteolysis

  • Compare glycosylation or other modifications through mass spectrometry

  • Evaluate thermostability differences using differential scanning fluorimetry

• Experimental design for comparative studies:

  • Include internal controls for each preparation

  • Normalize activity based on active protein concentration rather than total protein

  • Use multiple lots of each preparation to account for batch-to-batch variation

  • Design experiments that can detect subtle functional differences